Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations1499
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory363.2 KiB
Average record size in memory248.1 B

Variable types

Numeric14
Text3
Categorical12
DateTime2

Alerts

# is highly overall correlated with Unnamed: 0 and 6 other fieldsHigh correlation
Fuel in is highly overall correlated with Fuel outHigh correlation
Fuel out is highly overall correlated with Fuel inHigh correlation
KMs IN is highly overall correlated with KMs outHigh correlation
KMs out is highly overall correlated with KMs INHigh correlation
Unnamed: 0 is highly overall correlated with # and 6 other fieldsHigh correlation
cost is highly overall correlated with cost_categoryHigh correlation
cost_category is highly overall correlated with costHigh correlation
damage type is highly overall correlated with locationHigh correlation
dayIn is highly overall correlated with dayReadyHigh correlation
dayNIn is highly overall correlated with dayNReadyHigh correlation
dayNReady is highly overall correlated with dayNInHigh correlation
dayReady is highly overall correlated with dayInHigh correlation
delivered by is highly overall correlated with returned by and 2 other fieldsHigh correlation
location is highly overall correlated with damage type and 2 other fieldsHigh correlation
monthIn is highly overall correlated with # and 6 other fieldsHigh correlation
monthNIn is highly overall correlated with # and 6 other fieldsHigh correlation
monthNReady is highly overall correlated with # and 6 other fieldsHigh correlation
monthReady is highly overall correlated with # and 6 other fieldsHigh correlation
returned by is highly overall correlated with delivered by and 2 other fieldsHigh correlation
yearIn is highly overall correlated with # and 9 other fieldsHigh correlation
yearReady is highly overall correlated with # and 9 other fieldsHigh correlation
yearIn is highly imbalanced (59.1%)Imbalance
yearReady is highly imbalanced (59.1%)Imbalance
Unnamed: 0 is uniformly distributedUniform
# is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
# has unique valuesUnique
Fuel in has 27 (1.8%) zerosZeros
Fuel out has 25 (1.7%) zerosZeros
Fuel Diff has 1408 (93.9%) zerosZeros

Reproduction

Analysis started2024-10-24 11:57:02.792968
Analysis finished2024-10-24 11:58:23.186523
Duration1 minute and 20.39 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct1499
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean749
Minimum0
Maximum1498
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2024-10-24T14:58:23.407227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile74.9
Q1374.5
median749
Q31123.5
95-th percentile1423.1
Maximum1498
Range1498
Interquartile range (IQR)749

Descriptive statistics

Standard deviation432.86834
Coefficient of variation (CV)0.57792836
Kurtosis-1.2
Mean749
Median Absolute Deviation (MAD)375
Skewness0
Sum1122751
Variance187375
MonotonicityStrictly increasing
2024-10-24T14:58:23.694336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.1%
996 1
 
0.1%
1005 1
 
0.1%
1004 1
 
0.1%
1003 1
 
0.1%
1002 1
 
0.1%
1001 1
 
0.1%
1000 1
 
0.1%
999 1
 
0.1%
998 1
 
0.1%
Other values (1489) 1489
99.3%
ValueCountFrequency (%)
0 1
0.1%
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
ValueCountFrequency (%)
1498 1
0.1%
1497 1
0.1%
1496 1
0.1%
1495 1
0.1%
1494 1
0.1%
1493 1
0.1%
1492 1
0.1%
1491 1
0.1%
1490 1
0.1%
1489 1
0.1%

#
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct1499
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean750
Minimum1
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2024-10-24T14:58:23.990234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile75.9
Q1375.5
median750
Q31124.5
95-th percentile1424.1
Maximum1499
Range1498
Interquartile range (IQR)749

Descriptive statistics

Standard deviation432.86834
Coefficient of variation (CV)0.57715779
Kurtosis-1.2
Mean750
Median Absolute Deviation (MAD)375
Skewness0
Sum1124250
Variance187375
MonotonicityStrictly increasing
2024-10-24T14:58:24.307617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
997 1
 
0.1%
1006 1
 
0.1%
1005 1
 
0.1%
1004 1
 
0.1%
1003 1
 
0.1%
1002 1
 
0.1%
1001 1
 
0.1%
1000 1
 
0.1%
999 1
 
0.1%
Other values (1489) 1489
99.3%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1499 1
0.1%
1498 1
0.1%
1497 1
0.1%
1496 1
0.1%
1495 1
0.1%
1494 1
0.1%
1493 1
0.1%
1492 1
0.1%
1491 1
0.1%
1490 1
0.1%
Distinct337
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
2024-10-24T14:58:25.186523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length14
Median length8
Mean length8.0080053
Min length8

Characters and Unicode

Total characters12004
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)4.8%

Sample

1st row70-29280
2nd row70-26587
3rd row70-25180
4th row70-26523
5th row70-30719
ValueCountFrequency (%)
70-28946 13
 
0.9%
70-24166 13
 
0.9%
70-24837 13
 
0.9%
70-28698 13
 
0.9%
70-24118 12
 
0.8%
70-28940 12
 
0.8%
70-26840 12
 
0.8%
70-25198 12
 
0.8%
70-24549 12
 
0.8%
70-28286 11
 
0.7%
Other values (324) 1376
91.8%
2024-10-24T14:58:26.018555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 1877
15.6%
0 1752
14.6%
2 1609
13.4%
- 1500
12.5%
5 895
7.5%
3 769
6.4%
6 766
6.4%
4 765
6.4%
9 757
6.3%
8 665
 
5.5%
Other values (2) 649
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10494
87.4%
Dash Punctuation 1500
 
12.5%
Space Separator 10
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 1877
17.9%
0 1752
16.7%
2 1609
15.3%
5 895
8.5%
3 769
7.3%
6 766
7.3%
4 765
7.3%
9 757
7.2%
8 665
 
6.3%
1 639
 
6.1%
Dash Punctuation
ValueCountFrequency (%)
- 1500
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12004
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 1877
15.6%
0 1752
14.6%
2 1609
13.4%
- 1500
12.5%
5 895
7.5%
3 769
6.4%
6 766
6.4%
4 765
6.4%
9 757
6.3%
8 665
 
5.5%
Other values (2) 649
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12004
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 1877
15.6%
0 1752
14.6%
2 1609
13.4%
- 1500
12.5%
5 895
7.5%
3 769
6.4%
6 766
6.4%
4 765
6.4%
9 757
6.3%
8 665
 
5.5%
Other values (2) 649
 
5.4%

car
Categorical

Distinct25
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
FORTUNER
232 
CERATO
178 
COROLLA
153 
H 1
101 
HILUX
97 
Other values (20)
738 

Length

Max length10
Median length7
Mean length5.9239493
Min length3

Characters and Unicode

Total characters8880
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowTUCSAN
2nd rowELANTRA
3rd rowAVANZA
4th rowFLUENCE
5th rowFLUENCE

Common Values

ValueCountFrequency (%)
FORTUNER 232
15.5%
CERATO 178
11.9%
COROLLA 153
10.2%
H 1 101
 
6.7%
HILUX 97
 
6.5%
ELANTRA 87
 
5.8%
CAMRY 86
 
5.7%
SPARK 78
 
5.2%
RIO 70
 
4.7%
AVANZA 62
 
4.1%
Other values (15) 355
23.7%

Length

2024-10-24T14:58:26.319336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fortuner 232
14.1%
cerato 178
 
10.8%
corolla 156
 
9.5%
h 101
 
6.1%
1 101
 
6.1%
hilux 97
 
5.9%
elantra 87
 
5.3%
camry 86
 
5.2%
spark 78
 
4.7%
rio 70
 
4.3%
Other values (17) 459
27.9%

Most occurring characters

ValueCountFrequency (%)
R 1315
14.8%
A 1036
11.7%
O 921
10.4%
E 714
 
8.0%
T 592
 
6.7%
C 569
 
6.4%
L 551
 
6.2%
N 484
 
5.5%
U 448
 
5.0%
I 317
 
3.6%
Other values (16) 1933
21.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8538
96.1%
Decimal Number 183
 
2.1%
Space Separator 159
 
1.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 1315
15.4%
A 1036
12.1%
O 921
10.8%
E 714
8.4%
T 592
 
6.9%
C 569
 
6.7%
L 551
 
6.5%
N 484
 
5.7%
U 448
 
5.2%
I 317
 
3.7%
Other values (12) 1591
18.6%
Decimal Number
ValueCountFrequency (%)
1 138
75.4%
0 37
 
20.2%
4 8
 
4.4%
Space Separator
ValueCountFrequency (%)
159
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8538
96.1%
Common 342
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 1315
15.4%
A 1036
12.1%
O 921
10.8%
E 714
8.4%
T 592
 
6.9%
C 569
 
6.7%
L 551
 
6.5%
N 484
 
5.7%
U 448
 
5.2%
I 317
 
3.7%
Other values (12) 1591
18.6%
Common
ValueCountFrequency (%)
159
46.5%
1 138
40.4%
0 37
 
10.8%
4 8
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 1315
14.8%
A 1036
11.7%
O 921
10.4%
E 714
 
8.0%
T 592
 
6.7%
C 569
 
6.4%
L 551
 
6.2%
N 484
 
5.5%
U 448
 
5.0%
I 317
 
3.6%
Other values (16) 1933
21.8%

damage type
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
غيار زيت
594 
اصلاح مكانيك
466 
اصلاح بودي
229 
اصلاح كوشوك
124 
اصلاح كهرباء
 
46
Other values (2)
 
40

Length

Max length13
Median length12
Mean length10.271514
Min length8

Characters and Unicode

Total characters15397
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowاصلاح بودي
2nd rowاصلاح بودي
3rd rowاصلاح مكانيك
4th rowاصلاح بودي
5th rowغيار زيت

Common Values

ValueCountFrequency (%)
غيار زيت 594
39.6%
اصلاح مكانيك 466
31.1%
اصلاح بودي 229
 
15.3%
اصلاح كوشوك 124
 
8.3%
اصلاح كهرباء 46
 
3.1%
اصلاح زجاج 21
 
1.4%
اصلاح فرش 19
 
1.3%

Length

2024-10-24T14:58:26.543945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T14:58:26.994141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
اصلاح 905
30.2%
غيار 594
19.8%
زيت 594
19.8%
مكانيك 466
15.5%
بودي 229
 
7.6%
كوشوك 124
 
4.1%
كهرباء 46
 
1.5%
زجاج 21
 
0.7%
فرش 19
 
0.6%

Most occurring characters

ValueCountFrequency (%)
ا 2937
19.1%
1965
12.8%
ي 1883
12.2%
ك 1226
8.0%
ص 905
 
5.9%
ل 905
 
5.9%
ح 905
 
5.9%
ر 659
 
4.3%
ز 615
 
4.0%
غ 594
 
3.9%
Other values (11) 2803
18.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13432
87.2%
Space Separator 1965
 
12.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 2937
21.9%
ي 1883
14.0%
ك 1226
9.1%
ص 905
 
6.7%
ل 905
 
6.7%
ح 905
 
6.7%
ر 659
 
4.9%
ز 615
 
4.6%
غ 594
 
4.4%
ت 594
 
4.4%
Other values (10) 2209
16.4%
Space Separator
ValueCountFrequency (%)
1965
100.0%

Most occurring scripts

ValueCountFrequency (%)
Arabic 13432
87.2%
Common 1965
 
12.8%

Most frequent character per script

Arabic
ValueCountFrequency (%)
ا 2937
21.9%
ي 1883
14.0%
ك 1226
9.1%
ص 905
 
6.7%
ل 905
 
6.7%
ح 905
 
6.7%
ر 659
 
4.9%
ز 615
 
4.6%
غ 594
 
4.4%
ت 594
 
4.4%
Other values (10) 2209
16.4%
Common
ValueCountFrequency (%)
1965
100.0%

Most occurring blocks

ValueCountFrequency (%)
Arabic 13432
87.2%
ASCII 1965
 
12.8%

Most frequent character per block

Arabic
ValueCountFrequency (%)
ا 2937
21.9%
ي 1883
14.0%
ك 1226
9.1%
ص 905
 
6.7%
ل 905
 
6.7%
ح 905
 
6.7%
ر 659
 
4.9%
ز 615
 
4.6%
غ 594
 
4.4%
ت 594
 
4.4%
Other values (10) 2209
16.4%
ASCII
ValueCountFrequency (%)
1965
100.0%
Distinct301
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Minimum2015-01-01 00:00:00
Maximum2016-02-03 00:00:00
2024-10-24T14:58:27.350586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:27.657227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

KMs IN
Real number (ℝ)

HIGH CORRELATION 

Distinct1423
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63581.833
Minimum390
Maximum754935
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2024-10-24T14:58:27.964844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum390
5-th percentile6823.8
Q143710.5
median65890
Q382080
95-th percentile106887.2
Maximum754935
Range754545
Interquartile range (IQR)38369.5

Descriptive statistics

Standard deviation40221.634
Coefficient of variation (CV)0.63259634
Kurtosis110.99153
Mean63581.833
Median Absolute Deviation (MAD)18579
Skewness6.9516122
Sum95309167
Variance1.6177799 × 109
MonotonicityNot monotonic
2024-10-24T14:58:28.248047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88975 3
 
0.2%
65195 2
 
0.1%
59400 2
 
0.1%
98565 2
 
0.1%
49514 2
 
0.1%
68992 2
 
0.1%
86925 2
 
0.1%
78756 2
 
0.1%
66700 2
 
0.1%
70835 2
 
0.1%
Other values (1413) 1478
98.6%
ValueCountFrequency (%)
390 1
0.1%
790 1
0.1%
1741 1
0.1%
1835 1
0.1%
1902 1
0.1%
2085 1
0.1%
2160 1
0.1%
2274 1
0.1%
2487 1
0.1%
2509 1
0.1%
ValueCountFrequency (%)
754935 1
0.1%
716952 1
0.1%
481725 1
0.1%
194840 1
0.1%
191800 1
0.1%
162300 1
0.1%
155384 1
0.1%
154459 1
0.1%
151982 1
0.1%
148130 1
0.1%

Fuel in
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30859573
Minimum0
Maximum1
Zeros27
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2024-10-24T14:58:28.489258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.25
median0.25
Q30.38
95-th percentile0.38
Maximum1
Range1
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.11175463
Coefficient of variation (CV)0.36213927
Kurtosis10.110095
Mean0.30859573
Median Absolute Deviation (MAD)0
Skewness1.7630578
Sum462.585
Variance0.012489098
MonotonicityNot monotonic
2024-10-24T14:58:28.683594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.25 780
52.0%
0.38 583
38.9%
0.13 46
 
3.1%
0.5 32
 
2.1%
0 27
 
1.8%
0.63 9
 
0.6%
0.75 9
 
0.6%
1 8
 
0.5%
0.88 4
 
0.3%
0.125 1
 
0.1%
ValueCountFrequency (%)
0 27
 
1.8%
0.125 1
 
0.1%
0.13 46
 
3.1%
0.25 780
52.0%
0.38 583
38.9%
0.5 32
 
2.1%
0.63 9
 
0.6%
0.75 9
 
0.6%
0.88 4
 
0.3%
1 8
 
0.5%
ValueCountFrequency (%)
1 8
 
0.5%
0.88 4
 
0.3%
0.75 9
 
0.6%
0.63 9
 
0.6%
0.5 32
 
2.1%
0.38 583
38.9%
0.25 780
52.0%
0.13 46
 
3.1%
0.125 1
 
0.1%
0 27
 
1.8%
Distinct320
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Minimum2015-01-01 00:00:00
Maximum2016-02-03 00:00:00
2024-10-24T14:58:28.933594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:29.250000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

KMs out
Real number (ℝ)

HIGH CORRELATION 

Distinct1427
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63590.732
Minimum400
Maximum754945
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2024-10-24T14:58:29.552734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum400
5-th percentile6834.4
Q143724.5
median65897
Q382088
95-th percentile106896.2
Maximum754945
Range754545
Interquartile range (IQR)38363.5

Descriptive statistics

Standard deviation40221.529
Coefficient of variation (CV)0.63250614
Kurtosis110.99291
Mean63590.732
Median Absolute Deviation (MAD)18580
Skewness6.9516636
Sum95322507
Variance1.6177714 × 109
MonotonicityNot monotonic
2024-10-24T14:58:29.849609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5262 3
 
0.2%
88980 3
 
0.2%
63562 3
 
0.2%
50600 3
 
0.2%
53523 2
 
0.1%
51255 2
 
0.1%
77767 2
 
0.1%
69953 2
 
0.1%
94739 2
 
0.1%
94720 2
 
0.1%
Other values (1417) 1475
98.4%
ValueCountFrequency (%)
400 1
0.1%
803 1
0.1%
1749 1
0.1%
1846 1
0.1%
1911 1
0.1%
2097 1
0.1%
2165 1
0.1%
2280 1
0.1%
2500 1
0.1%
2518 1
0.1%
ValueCountFrequency (%)
754945 1
0.1%
716961 1
0.1%
481731 1
0.1%
194848 1
0.1%
191810 1
0.1%
162304 1
0.1%
155390 1
0.1%
154465 1
0.1%
151991 1
0.1%
148140 1
0.1%

KMs Diff
Real number (ℝ)

Distinct39
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8992662
Minimum0
Maximum71
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2024-10-24T14:58:30.146484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q16
median9
Q310
95-th percentile16
Maximum71
Range71
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.8396028
Coefficient of variation (CV)0.54382043
Kurtosis40.231497
Mean8.8992662
Median Absolute Deviation (MAD)2
Skewness4.8501891
Sum13340
Variance23.421755
MonotonicityNot monotonic
2024-10-24T14:58:30.967773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
9 367
24.5%
5 184
12.3%
8 182
12.1%
7 175
11.7%
10 149
9.9%
6 134
 
8.9%
11 60
 
4.0%
4 53
 
3.5%
12 32
 
2.1%
13 24
 
1.6%
Other values (29) 139
 
9.3%
ValueCountFrequency (%)
0 1
 
0.1%
1 1
 
0.1%
2 2
 
0.1%
3 11
 
0.7%
4 53
 
3.5%
5 184
12.3%
6 134
 
8.9%
7 175
11.7%
8 182
12.1%
9 367
24.5%
ValueCountFrequency (%)
71 1
 
0.1%
58 1
 
0.1%
55 1
 
0.1%
50 1
 
0.1%
42 1
 
0.1%
41 1
 
0.1%
38 2
0.1%
34 1
 
0.1%
32 3
0.2%
30 3
0.2%

Fuel out
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31420947
Minimum0
Maximum1
Zeros25
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2024-10-24T14:58:31.203125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.25
median0.25
Q30.38
95-th percentile0.392
Maximum1
Range1
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.11511977
Coefficient of variation (CV)0.36637907
Kurtosis10.005328
Mean0.31420947
Median Absolute Deviation (MAD)0
Skewness1.9121434
Sum471
Variance0.013252562
MonotonicityNot monotonic
2024-10-24T14:58:31.392578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.25 769
51.3%
0.38 595
39.7%
0.5 40
 
2.7%
0.13 35
 
2.3%
0 25
 
1.7%
0.63 11
 
0.7%
0.88 9
 
0.6%
1 8
 
0.5%
0.75 7
 
0.5%
ValueCountFrequency (%)
0 25
 
1.7%
0.13 35
 
2.3%
0.25 769
51.3%
0.38 595
39.7%
0.5 40
 
2.7%
0.63 11
 
0.7%
0.75 7
 
0.5%
0.88 9
 
0.6%
1 8
 
0.5%
ValueCountFrequency (%)
1 8
 
0.5%
0.88 9
 
0.6%
0.75 7
 
0.5%
0.63 11
 
0.7%
0.5 40
 
2.7%
0.38 595
39.7%
0.25 769
51.3%
0.13 35
 
2.3%
0 25
 
1.7%

Fuel Diff
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0056137425
Minimum-0.38
Maximum0.38
Zeros1408
Zeros (%)93.9%
Negative16
Negative (%)1.1%
Memory size11.8 KiB
2024-10-24T14:58:31.587891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.38
5-th percentile0
Q10
median0
Q30
95-th percentile0.012
Maximum0.38
Range0.76
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.040568271
Coefficient of variation (CV)7.2265999
Kurtosis31.636671
Mean0.0056137425
Median Absolute Deviation (MAD)0
Skewness2.2936848
Sum8.415
Variance0.0016457846
MonotonicityNot monotonic
2024-10-24T14:58:31.794922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 1408
93.9%
0.13 39
 
2.6%
0.12 25
 
1.7%
-0.13 8
 
0.5%
0.25 7
 
0.5%
-0.25 4
 
0.3%
-0.12 3
 
0.2%
0.37 2
 
0.1%
-0.38 1
 
0.1%
0.38 1
 
0.1%
ValueCountFrequency (%)
-0.38 1
 
0.1%
-0.25 4
 
0.3%
-0.13 8
 
0.5%
-0.12 3
 
0.2%
0 1408
93.9%
0.12 25
 
1.7%
0.13 39
 
2.6%
0.25 7
 
0.5%
0.255 1
 
0.1%
0.37 2
 
0.1%
ValueCountFrequency (%)
0.38 1
 
0.1%
0.37 2
 
0.1%
0.255 1
 
0.1%
0.25 7
 
0.5%
0.13 39
 
2.6%
0.12 25
 
1.7%
0 1408
93.9%
-0.12 3
 
0.2%
-0.13 8
 
0.5%
-0.25 4
 
0.3%

cost
Real number (ℝ)

HIGH CORRELATION 

Distinct166
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.94663
Minimum2
Maximum2500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2024-10-24T14:58:32.068359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile20
Q121
median50
Q3102
95-th percentile361.3
Maximum2500
Range2498
Interquartile range (IQR)81

Descriptive statistics

Standard deviation166.28248
Coefficient of variation (CV)1.5404138
Kurtosis51.203579
Mean107.94663
Median Absolute Deviation (MAD)29
Skewness5.3691081
Sum161812
Variance27649.862
MonotonicityNot monotonic
2024-10-24T14:58:32.352539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 451
30.1%
281 83
 
5.5%
92 81
 
5.4%
102 75
 
5.0%
20 38
 
2.5%
50 36
 
2.4%
80 35
 
2.3%
60 31
 
2.1%
19 25
 
1.7%
25 25
 
1.7%
Other values (156) 619
41.3%
ValueCountFrequency (%)
2 3
 
0.2%
4 1
 
0.1%
5 3
 
0.2%
8 1
 
0.1%
10 4
 
0.3%
12 1
 
0.1%
13 3
 
0.2%
14 2
 
0.1%
15 23
1.5%
16 2
 
0.1%
ValueCountFrequency (%)
2500 1
0.1%
2100 1
0.1%
1500 1
0.1%
1250 1
0.1%
1150 1
0.1%
1140 1
0.1%
1110 1
0.1%
1000 2
0.1%
900 1
0.1%
800 1
0.1%

location
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
الغزاوي
680 
القسطل
202 
المركزية
149 
معاذ عليان
109 
4 جيد
81 
Other values (27)
278 

Length

Max length19
Median length18
Mean length7.3035357
Min length3

Characters and Unicode

Total characters10948
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowالمركزية
2nd rowالمركزية
3rd rowالمركزية
4th rowابو خضر
5th rowالمركزية

Common Values

ValueCountFrequency (%)
الغزاوي 680
45.4%
القسطل 202
 
13.5%
المركزية 149
 
9.9%
معاذ عليان 109
 
7.3%
4 جيد 81
 
5.4%
امجد العطاري 46
 
3.1%
شارلي 26
 
1.7%
قسطل 23
 
1.5%
ابو نعمه 22
 
1.5%
هانكونك 20
 
1.3%
Other values (22) 141
 
9.4%

Length

2024-10-24T14:58:32.621094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
الغزاوي 680
37.6%
القسطل 202
 
11.2%
المركزية 149
 
8.2%
معاذ 109
 
6.0%
عليان 109
 
6.0%
4 81
 
4.5%
جيد 81
 
4.5%
امجد 46
 
2.5%
العطاري 46
 
2.5%
ابو 30
 
1.7%
Other values (35) 277
15.3%

Most occurring characters

ValueCountFrequency (%)
ا 2314
21.1%
ل 1566
14.3%
ي 1204
11.0%
ز 886
 
8.1%
و 771
 
7.0%
غ 694
 
6.3%
م 408
 
3.7%
311
 
2.8%
ع 310
 
2.8%
ط 286
 
2.6%
Other values (20) 2198
20.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10528
96.2%
Space Separator 311
 
2.8%
Decimal Number 81
 
0.7%
Math Symbol 28
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 2314
22.0%
ل 1566
14.9%
ي 1204
11.4%
ز 886
 
8.4%
و 771
 
7.3%
غ 694
 
6.6%
م 408
 
3.9%
ع 310
 
2.9%
ط 286
 
2.7%
ر 283
 
2.7%
Other values (17) 1806
17.2%
Space Separator
ValueCountFrequency (%)
311
100.0%
Decimal Number
ValueCountFrequency (%)
4 81
100.0%
Math Symbol
ValueCountFrequency (%)
+ 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Arabic 10528
96.2%
Common 420
 
3.8%

Most frequent character per script

Arabic
ValueCountFrequency (%)
ا 2314
22.0%
ل 1566
14.9%
ي 1204
11.4%
ز 886
 
8.4%
و 771
 
7.3%
غ 694
 
6.6%
م 408
 
3.9%
ع 310
 
2.9%
ط 286
 
2.7%
ر 283
 
2.7%
Other values (17) 1806
17.2%
Common
ValueCountFrequency (%)
311
74.0%
4 81
 
19.3%
+ 28
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Arabic 10528
96.2%
ASCII 420
 
3.8%

Most frequent character per block

Arabic
ValueCountFrequency (%)
ا 2314
22.0%
ل 1566
14.9%
ي 1204
11.4%
ز 886
 
8.4%
و 771
 
7.3%
غ 694
 
6.6%
م 408
 
3.9%
ع 310
 
2.9%
ط 286
 
2.7%
ر 283
 
2.7%
Other values (17) 1806
17.2%
ASCII
ValueCountFrequency (%)
311
74.0%
4 81
 
19.3%
+ 28
 
6.7%
Distinct55
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
2024-10-24T14:58:32.946289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length35
Median length2
Mean length4.7024683
Min length2

Characters and Unicode

Total characters7049
Distinct characters73
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)1.3%

Sample

1st rowXe
2nd rowXe
3rd rowXe
4th rowXe
5th rowXe
ValueCountFrequency (%)
xe 1097
60.8%
jordan 96
 
5.3%
transports 93
 
5.2%
vestas 65
 
3.6%
gunsayil 27
 
1.5%
the 26
 
1.4%
save 23
 
1.3%
children 23
 
1.3%
support 22
 
1.2%
services 22
 
1.2%
Other values (77) 310
 
17.2%
2024-10-24T14:58:33.546875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1387
19.7%
X 1097
15.6%
r 464
 
6.6%
s 439
 
6.2%
a 394
 
5.6%
n 345
 
4.9%
o 324
 
4.6%
305
 
4.3%
t 257
 
3.6%
i 256
 
3.6%
Other values (63) 1781
25.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4780
67.8%
Uppercase Letter 1777
 
25.2%
Space Separator 305
 
4.3%
Other Letter 186
 
2.6%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1387
29.0%
r 464
 
9.7%
s 439
 
9.2%
a 394
 
8.2%
n 345
 
7.2%
o 324
 
6.8%
t 257
 
5.4%
i 256
 
5.4%
d 191
 
4.0%
p 150
 
3.1%
Other values (14) 573
12.0%
Uppercase Letter
ValueCountFrequency (%)
X 1097
61.7%
T 124
 
7.0%
S 101
 
5.7%
J 96
 
5.4%
V 86
 
4.8%
G 39
 
2.2%
C 34
 
1.9%
L 28
 
1.6%
A 27
 
1.5%
W 24
 
1.4%
Other values (14) 121
 
6.8%
Other Letter
ValueCountFrequency (%)
ه 29
15.6%
ا 23
12.4%
ك 20
10.8%
ل 20
10.8%
ي 17
9.1%
ر 14
7.5%
ن 12
6.5%
س 8
 
4.3%
م 8
 
4.3%
و 6
 
3.2%
Other values (13) 29
15.6%
Space Separator
ValueCountFrequency (%)
305
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6557
93.0%
Common 306
 
4.3%
Arabic 186
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1387
21.2%
X 1097
16.7%
r 464
 
7.1%
s 439
 
6.7%
a 394
 
6.0%
n 345
 
5.3%
o 324
 
4.9%
t 257
 
3.9%
i 256
 
3.9%
d 191
 
2.9%
Other values (38) 1403
21.4%
Arabic
ValueCountFrequency (%)
ه 29
15.6%
ا 23
12.4%
ك 20
10.8%
ل 20
10.8%
ي 17
9.1%
ر 14
7.5%
ن 12
6.5%
س 8
 
4.3%
م 8
 
4.3%
و 6
 
3.2%
Other values (13) 29
15.6%
Common
ValueCountFrequency (%)
305
99.7%
/ 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6863
97.4%
Arabic 186
 
2.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1387
20.2%
X 1097
16.0%
r 464
 
6.8%
s 439
 
6.4%
a 394
 
5.7%
n 345
 
5.0%
o 324
 
4.7%
305
 
4.4%
t 257
 
3.7%
i 256
 
3.7%
Other values (40) 1595
23.2%
Arabic
ValueCountFrequency (%)
ه 29
15.6%
ا 23
12.4%
ك 20
10.8%
ل 20
10.8%
ي 17
9.1%
ر 14
7.5%
ن 12
6.5%
س 8
 
4.3%
م 8
 
4.3%
و 6
 
3.2%
Other values (13) 29
15.6%

delivered by
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Mohamad Hane
290 
Amjad
223 
Dirar
196 
Mohamad J
172 
Mohamad Qasim
117 
Other values (25)
501 

Length

Max length13
Median length12
Mean length7.5923949
Min length3

Characters and Unicode

Total characters11381
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowOmar M
2nd rowMaen
3rd rowMohamad J
4th rowMohamad Qasim
5th rowMohamad Qasim

Common Values

ValueCountFrequency (%)
Mohamad Hane 290
19.3%
Amjad 223
14.9%
Dirar 196
13.1%
Mohamad J 172
11.5%
Mohamad Qasim 117
7.8%
Abdalla 80
 
5.3%
Khalil 63
 
4.2%
Gad 63
 
4.2%
Saddam 41
 
2.7%
Hamza 38
 
2.5%
Other values (20) 216
14.4%

Length

2024-10-24T14:58:33.807617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mohamad 579
27.5%
hane 290
13.8%
amjad 223
 
10.6%
dirar 196
 
9.3%
j 172
 
8.2%
qasim 120
 
5.7%
abdalla 80
 
3.8%
khalil 63
 
3.0%
gad 63
 
3.0%
saddam 41
 
1.9%
Other values (25) 276
13.1%

Most occurring characters

ValueCountFrequency (%)
a 2635
23.2%
d 1103
9.7%
m 1079
 
9.5%
h 715
 
6.3%
M 640
 
5.6%
o 616
 
5.4%
604
 
5.3%
r 468
 
4.1%
i 428
 
3.8%
A 368
 
3.2%
Other values (28) 2725
23.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8674
76.2%
Uppercase Letter 2103
 
18.5%
Space Separator 604
 
5.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2635
30.4%
d 1103
12.7%
m 1079
12.4%
h 715
 
8.2%
o 616
 
7.1%
r 468
 
5.4%
i 428
 
4.9%
e 359
 
4.1%
l 349
 
4.0%
n 319
 
3.7%
Other values (10) 603
 
7.0%
Uppercase Letter
ValueCountFrequency (%)
M 640
30.4%
A 368
17.5%
H 328
15.6%
D 196
 
9.3%
J 186
 
8.8%
Q 120
 
5.7%
K 63
 
3.0%
G 63
 
3.0%
S 51
 
2.4%
T 33
 
1.6%
Other values (7) 55
 
2.6%
Space Separator
ValueCountFrequency (%)
604
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10777
94.7%
Common 604
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2635
24.5%
d 1103
10.2%
m 1079
10.0%
h 715
 
6.6%
M 640
 
5.9%
o 616
 
5.7%
r 468
 
4.3%
i 428
 
4.0%
A 368
 
3.4%
e 359
 
3.3%
Other values (27) 2366
22.0%
Common
ValueCountFrequency (%)
604
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2635
23.2%
d 1103
9.7%
m 1079
 
9.5%
h 715
 
6.3%
M 640
 
5.6%
o 616
 
5.4%
604
 
5.3%
r 468
 
4.1%
i 428
 
3.8%
A 368
 
3.2%
Other values (28) 2725
23.9%

returned by
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Mohammed Hane
290 
Amjad
223 
Dirar
196 
Mohamad J
170 
Gad
91 
Other values (25)
529 

Length

Max length13
Median length12
Mean length7.5096731
Min length3

Characters and Unicode

Total characters11257
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowOmar M
2nd rowMaen
3rd rowMohamad J
4th rowMohamad Qasim
5th rowOmar M

Common Values

ValueCountFrequency (%)
Mohammed Hane 290
19.3%
Amjad 223
14.9%
Dirar 196
13.1%
Mohamad J 170
11.3%
Gad 91
 
6.1%
Abdalla 81
 
5.4%
Mohamad Qasim 72
 
4.8%
Khalil 63
 
4.2%
Saddam 41
 
2.7%
Hamza 38
 
2.5%
Other values (20) 234
15.6%

Length

2024-10-24T14:58:34.017578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mohammed 290
14.0%
hane 290
14.0%
mohamad 242
11.7%
amjad 223
10.8%
dirar 196
9.5%
j 170
8.2%
gad 91
 
4.4%
abdalla 81
 
3.9%
qasim 75
 
3.6%
khalil 63
 
3.0%
Other values (26) 345
16.7%

Most occurring characters

ValueCountFrequency (%)
a 2254
20.0%
m 1289
11.5%
d 1087
9.7%
h 670
 
6.0%
e 655
 
5.8%
M 611
 
5.4%
o 571
 
5.1%
567
 
5.0%
r 478
 
4.2%
i 383
 
3.4%
Other values (28) 2692
23.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8624
76.6%
Uppercase Letter 2066
 
18.4%
Space Separator 567
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2254
26.1%
m 1289
14.9%
d 1087
12.6%
h 670
 
7.8%
e 655
 
7.6%
o 571
 
6.6%
r 478
 
5.5%
i 383
 
4.4%
l 351
 
4.1%
n 325
 
3.8%
Other values (10) 561
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
M 611
29.6%
A 369
17.9%
H 328
15.9%
D 196
 
9.5%
J 184
 
8.9%
G 91
 
4.4%
Q 75
 
3.6%
K 63
 
3.0%
S 51
 
2.5%
O 40
 
1.9%
Other values (7) 58
 
2.8%
Space Separator
ValueCountFrequency (%)
567
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10690
95.0%
Common 567
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2254
21.1%
m 1289
12.1%
d 1087
10.2%
h 670
 
6.3%
e 655
 
6.1%
M 611
 
5.7%
o 571
 
5.3%
r 478
 
4.5%
i 383
 
3.6%
A 369
 
3.5%
Other values (27) 2323
21.7%
Common
ValueCountFrequency (%)
567
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11257
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2254
20.0%
m 1289
11.5%
d 1087
9.7%
h 670
 
6.0%
e 655
 
5.8%
M 611
 
5.4%
o 571
 
5.1%
567
 
5.0%
r 478
 
4.2%
i 383
 
3.4%
Other values (28) 2692
23.9%

notes
Text

Distinct666
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
2024-10-24T14:58:34.568359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length105
Median length62
Mean length17.46431
Min length3

Characters and Unicode

Total characters26179
Distinct characters47
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique533 ?
Unique (%)35.6%

Sample

1st rowاصلاح بودي ضربة باب خلفي شمال المركزية
2nd rowاصلاح بودي ضربة مرش يمين المركزية
3rd rowاصلاح حميان المركزية
4th rowغيار مراة كاملة شركة رنوت
5th rowغيار زيت + اصلاح مرشة الغزاوي/ المركزية
ValueCountFrequency (%)
غيار 825
 
16.8%
زيت 705
 
14.3%
231
 
4.7%
فلتر 193
 
3.9%
اصلاح 163
 
3.3%
بريك 162
 
3.3%
امامي 142
 
2.9%
خلفي 83
 
1.7%
القسطل 82
 
1.7%
كامل 81
 
1.6%
Other values (554) 2255
45.8%
2024-10-24T14:58:35.396484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3676
14.0%
ي 3339
12.8%
ا 3213
12.3%
ر 2110
 
8.1%
ت 1648
 
6.3%
ل 1235
 
4.7%
م 1028
 
3.9%
غ 1006
 
3.8%
ز 955
 
3.6%
+ 916
 
3.5%
Other values (37) 7053
26.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21418
81.8%
Space Separator 3676
 
14.0%
Math Symbol 916
 
3.5%
Other Punctuation 80
 
0.3%
Decimal Number 76
 
0.3%
Open Punctuation 5
 
< 0.1%
Close Punctuation 5
 
< 0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ي 3339
15.6%
ا 3213
15.0%
ر 2110
9.9%
ت 1648
 
7.7%
ل 1235
 
5.8%
م 1028
 
4.8%
غ 1006
 
4.7%
ز 955
 
4.5%
ك 899
 
4.2%
ب 806
 
3.8%
Other values (23) 5179
24.2%
Decimal Number
ValueCountFrequency (%)
2 33
43.4%
4 29
38.2%
1 12
 
15.8%
5 1
 
1.3%
3 1
 
1.3%
Lowercase Letter
ValueCountFrequency (%)
n 1
33.3%
r 1
33.3%
c 1
33.3%
Other Punctuation
ValueCountFrequency (%)
/ 78
97.5%
, 2
 
2.5%
Space Separator
ValueCountFrequency (%)
3676
100.0%
Math Symbol
ValueCountFrequency (%)
+ 916
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Arabic 21418
81.8%
Common 4758
 
18.2%
Latin 3
 
< 0.1%

Most frequent character per script

Arabic
ValueCountFrequency (%)
ي 3339
15.6%
ا 3213
15.0%
ر 2110
9.9%
ت 1648
 
7.7%
ل 1235
 
5.8%
م 1028
 
4.8%
غ 1006
 
4.7%
ز 955
 
4.5%
ك 899
 
4.2%
ب 806
 
3.8%
Other values (23) 5179
24.2%
Common
ValueCountFrequency (%)
3676
77.3%
+ 916
 
19.3%
/ 78
 
1.6%
2 33
 
0.7%
4 29
 
0.6%
1 12
 
0.3%
( 5
 
0.1%
) 5
 
0.1%
, 2
 
< 0.1%
5 1
 
< 0.1%
Latin
ValueCountFrequency (%)
n 1
33.3%
r 1
33.3%
c 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Arabic 21418
81.8%
ASCII 4761
 
18.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3676
77.2%
+ 916
 
19.2%
/ 78
 
1.6%
2 33
 
0.7%
4 29
 
0.6%
1 12
 
0.3%
( 5
 
0.1%
) 5
 
0.1%
, 2
 
< 0.1%
5 1
 
< 0.1%
Other values (4) 4
 
0.1%
Arabic
ValueCountFrequency (%)
ي 3339
15.6%
ا 3213
15.0%
ر 2110
9.9%
ت 1648
 
7.7%
ل 1235
 
5.8%
م 1028
 
4.8%
غ 1006
 
4.7%
ز 955
 
4.5%
ك 899
 
4.2%
ب 806
 
3.8%
Other values (23) 5179
24.2%

yearIn
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
2015
1376 
2016
 
123

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5996
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2015 1376
91.8%
2016 123
 
8.2%

Length

2024-10-24T14:58:35.654297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T14:58:35.874023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2015 1376
91.8%
2016 123
 
8.2%

Most occurring characters

ValueCountFrequency (%)
2 1499
25.0%
0 1499
25.0%
1 1499
25.0%
5 1376
22.9%
6 123
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5996
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1499
25.0%
0 1499
25.0%
1 1499
25.0%
5 1376
22.9%
6 123
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5996
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1499
25.0%
0 1499
25.0%
1 1499
25.0%
5 1376
22.9%
6 123
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1499
25.0%
0 1499
25.0%
1 1499
25.0%
5 1376
22.9%
6 123
 
2.1%

monthIn
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3722482
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2024-10-24T14:58:36.051758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4374329
Coefficient of variation (CV)0.53943801
Kurtosis-1.1312833
Mean6.3722482
Median Absolute Deviation (MAD)3
Skewness-0.064217675
Sum9552
Variance11.815945
MonotonicityNot monotonic
2024-10-24T14:58:36.240234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 198
13.2%
5 164
10.9%
8 152
10.1%
6 145
9.7%
9 133
8.9%
11 131
8.7%
7 112
7.5%
10 108
7.2%
3 98
6.5%
12 96
6.4%
Other values (2) 162
10.8%
ValueCountFrequency (%)
1 198
13.2%
2 74
 
4.9%
3 98
6.5%
4 88
5.9%
5 164
10.9%
6 145
9.7%
7 112
7.5%
8 152
10.1%
9 133
8.9%
10 108
7.2%
ValueCountFrequency (%)
12 96
6.4%
11 131
8.7%
10 108
7.2%
9 133
8.9%
8 152
10.1%
7 112
7.5%
6 145
9.7%
5 164
10.9%
4 88
5.9%
3 98
6.5%

monthNIn
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
January
198 
May
164 
August
152 
June
145 
September
133 
Other values (7)
707 

Length

Max length9
Median length7
Mean length6.0767178
Min length3

Characters and Unicode

Total characters9109
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJanuary
2nd rowJanuary
3rd rowJanuary
4th rowJanuary
5th rowJanuary

Common Values

ValueCountFrequency (%)
January 198
13.2%
May 164
10.9%
August 152
10.1%
June 145
9.7%
September 133
8.9%
November 131
8.7%
July 112
7.5%
October 108
7.2%
March 98
6.5%
December 96
6.4%
Other values (2) 162
10.8%

Length

2024-10-24T14:58:36.459961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
january 198
13.2%
may 164
10.9%
august 152
10.1%
june 145
9.7%
september 133
8.9%
november 131
8.7%
july 112
7.5%
october 108
7.2%
march 98
6.5%
december 96
6.4%
Other values (2) 162
10.8%

Most occurring characters

ValueCountFrequency (%)
e 1276
14.0%
r 1000
 
11.0%
u 833
 
9.1%
a 732
 
8.0%
y 548
 
6.0%
b 542
 
6.0%
J 455
 
5.0%
t 393
 
4.3%
m 360
 
4.0%
n 343
 
3.8%
Other values (16) 2627
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7610
83.5%
Uppercase Letter 1499
 
16.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1276
16.8%
r 1000
13.1%
u 833
10.9%
a 732
9.6%
y 548
7.2%
b 542
7.1%
t 393
 
5.2%
m 360
 
4.7%
n 343
 
4.5%
c 302
 
4.0%
Other values (8) 1281
16.8%
Uppercase Letter
ValueCountFrequency (%)
J 455
30.4%
M 262
17.5%
A 240
16.0%
S 133
 
8.9%
N 131
 
8.7%
O 108
 
7.2%
D 96
 
6.4%
F 74
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 9109
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1276
14.0%
r 1000
 
11.0%
u 833
 
9.1%
a 732
 
8.0%
y 548
 
6.0%
b 542
 
6.0%
J 455
 
5.0%
t 393
 
4.3%
m 360
 
4.0%
n 343
 
3.8%
Other values (16) 2627
28.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9109
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1276
14.0%
r 1000
 
11.0%
u 833
 
9.1%
a 732
 
8.0%
y 548
 
6.0%
b 542
 
6.0%
J 455
 
5.0%
t 393
 
4.3%
m 360
 
4.0%
n 343
 
3.8%
Other values (16) 2627
28.8%

dayIn
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.869246
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2024-10-24T14:58:36.693359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median15
Q322
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.8806195
Coefficient of variation (CV)0.59724746
Kurtosis-1.1149305
Mean14.869246
Median Absolute Deviation (MAD)7
Skewness0.062353286
Sum22289
Variance78.865402
MonotonicityNot monotonic
2024-10-24T14:58:36.920898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 122
 
8.1%
19 84
 
5.6%
11 62
 
4.1%
8 61
 
4.1%
5 57
 
3.8%
15 56
 
3.7%
12 56
 
3.7%
14 56
 
3.7%
10 55
 
3.7%
16 51
 
3.4%
Other values (21) 839
56.0%
ValueCountFrequency (%)
1 122
8.1%
2 49
3.3%
3 29
 
1.9%
4 32
 
2.1%
5 57
3.8%
6 44
 
2.9%
7 40
 
2.7%
8 61
4.1%
9 36
 
2.4%
10 55
3.7%
ValueCountFrequency (%)
31 33
2.2%
30 34
2.3%
29 40
2.7%
28 39
2.6%
27 40
2.7%
26 41
2.7%
25 47
3.1%
24 41
2.7%
23 50
3.3%
22 37
2.5%

dayNIn
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Tuesday
293 
Wednesday
262 
Thursday
241 
Sunday
236 
Monday
235 
Other values (2)
232 

Length

Max length9
Median length8
Mean length7.281521
Min length6

Characters and Unicode

Total characters10915
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWednesday
2nd rowWednesday
3rd rowWednesday
4th rowWednesday
5th rowWednesday

Common Values

ValueCountFrequency (%)
Tuesday 293
19.5%
Wednesday 262
17.5%
Thursday 241
16.1%
Sunday 236
15.7%
Monday 235
15.7%
Saturday 180
12.0%
Friday 52
 
3.5%

Length

2024-10-24T14:58:37.183594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T14:58:37.517578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
tuesday 293
19.5%
wednesday 262
17.5%
thursday 241
16.1%
sunday 236
15.7%
monday 235
15.7%
saturday 180
12.0%
friday 52
 
3.5%

Most occurring characters

ValueCountFrequency (%)
d 1761
16.1%
a 1679
15.4%
y 1499
13.7%
u 950
8.7%
e 817
7.5%
s 796
7.3%
n 733
6.7%
T 534
 
4.9%
r 473
 
4.3%
S 416
 
3.8%
Other values (7) 1257
11.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9416
86.3%
Uppercase Letter 1499
 
13.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 1761
18.7%
a 1679
17.8%
y 1499
15.9%
u 950
10.1%
e 817
8.7%
s 796
8.5%
n 733
7.8%
r 473
 
5.0%
h 241
 
2.6%
o 235
 
2.5%
Other values (2) 232
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
T 534
35.6%
S 416
27.8%
W 262
17.5%
M 235
15.7%
F 52
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 10915
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 1761
16.1%
a 1679
15.4%
y 1499
13.7%
u 950
8.7%
e 817
7.5%
s 796
7.3%
n 733
6.7%
T 534
 
4.9%
r 473
 
4.3%
S 416
 
3.8%
Other values (7) 1257
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10915
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 1761
16.1%
a 1679
15.4%
y 1499
13.7%
u 950
8.7%
e 817
7.5%
s 796
7.3%
n 733
6.7%
T 534
 
4.9%
r 473
 
4.3%
S 416
 
3.8%
Other values (7) 1257
11.5%

yearReady
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
2015
1376 
2016
 
123

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5996
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2015 1376
91.8%
2016 123
 
8.2%

Length

2024-10-24T14:58:37.799805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T14:58:38.027344image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2015 1376
91.8%
2016 123
 
8.2%

Most occurring characters

ValueCountFrequency (%)
2 1499
25.0%
0 1499
25.0%
1 1499
25.0%
5 1376
22.9%
6 123
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5996
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1499
25.0%
0 1499
25.0%
1 1499
25.0%
5 1376
22.9%
6 123
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5996
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1499
25.0%
0 1499
25.0%
1 1499
25.0%
5 1376
22.9%
6 123
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1499
25.0%
0 1499
25.0%
1 1499
25.0%
5 1376
22.9%
6 123
 
2.1%

monthReady
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3915944
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2024-10-24T14:58:38.568359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4279864
Coefficient of variation (CV)0.53632728
Kurtosis-1.1257111
Mean6.3915944
Median Absolute Deviation (MAD)3
Skewness-0.070771366
Sum9581
Variance11.751091
MonotonicityNot monotonic
2024-10-24T14:58:38.756836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 193
12.9%
5 162
10.8%
8 151
10.1%
6 141
9.4%
9 135
9.0%
11 131
8.7%
7 119
7.9%
10 108
7.2%
3 97
6.5%
12 96
6.4%
Other values (2) 166
11.1%
ValueCountFrequency (%)
1 193
12.9%
2 76
 
5.1%
3 97
6.5%
4 90
6.0%
5 162
10.8%
6 141
9.4%
7 119
7.9%
8 151
10.1%
9 135
9.0%
10 108
7.2%
ValueCountFrequency (%)
12 96
6.4%
11 131
8.7%
10 108
7.2%
9 135
9.0%
8 151
10.1%
7 119
7.9%
6 141
9.4%
5 162
10.8%
4 90
6.0%
3 97
6.5%

monthNReady
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
January
193 
May
162 
August
151 
June
141 
September
135 
Other values (7)
717 

Length

Max length9
Median length7
Mean length6.0793863
Min length3

Characters and Unicode

Total characters9113
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJanuary
2nd rowJanuary
3rd rowJanuary
4th rowJanuary
5th rowJanuary

Common Values

ValueCountFrequency (%)
January 193
12.9%
May 162
10.8%
August 151
10.1%
June 141
9.4%
September 135
9.0%
November 131
8.7%
July 119
7.9%
October 108
7.2%
March 97
6.5%
December 96
6.4%
Other values (2) 166
11.1%

Length

2024-10-24T14:58:38.978516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
january 193
12.9%
may 162
10.8%
august 151
10.1%
june 141
9.4%
september 135
9.0%
november 131
8.7%
july 119
7.9%
october 108
7.2%
march 97
6.5%
december 96
6.4%
Other values (2) 166
11.1%

Most occurring characters

ValueCountFrequency (%)
e 1280
14.0%
r 1002
 
11.0%
u 831
 
9.1%
a 721
 
7.9%
y 550
 
6.0%
b 546
 
6.0%
J 453
 
5.0%
t 394
 
4.3%
m 362
 
4.0%
n 334
 
3.7%
Other values (16) 2640
29.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7614
83.6%
Uppercase Letter 1499
 
16.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1280
16.8%
r 1002
13.2%
u 831
10.9%
a 721
9.5%
y 550
7.2%
b 546
7.2%
t 394
 
5.2%
m 362
 
4.8%
n 334
 
4.4%
c 301
 
4.0%
Other values (8) 1293
17.0%
Uppercase Letter
ValueCountFrequency (%)
J 453
30.2%
M 259
17.3%
A 241
16.1%
S 135
 
9.0%
N 131
 
8.7%
O 108
 
7.2%
D 96
 
6.4%
F 76
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 9113
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1280
14.0%
r 1002
 
11.0%
u 831
 
9.1%
a 721
 
7.9%
y 550
 
6.0%
b 546
 
6.0%
J 453
 
5.0%
t 394
 
4.3%
m 362
 
4.0%
n 334
 
3.7%
Other values (16) 2640
29.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9113
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1280
14.0%
r 1002
 
11.0%
u 831
 
9.1%
a 721
 
7.9%
y 550
 
6.0%
b 546
 
6.0%
J 453
 
5.0%
t 394
 
4.3%
m 362
 
4.0%
n 334
 
3.7%
Other values (16) 2640
29.0%

dayReady
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.164109
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2024-10-24T14:58:39.211914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median15
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8845747
Coefficient of variation (CV)0.58589492
Kurtosis-1.1279854
Mean15.164109
Median Absolute Deviation (MAD)8
Skewness0.02832714
Sum22731
Variance78.935667
MonotonicityNot monotonic
2024-10-24T14:58:39.439453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 109
 
7.3%
19 67
 
4.5%
15 67
 
4.5%
16 61
 
4.1%
8 61
 
4.1%
12 55
 
3.7%
5 55
 
3.7%
14 54
 
3.6%
11 52
 
3.5%
24 52
 
3.5%
Other values (21) 866
57.8%
ValueCountFrequency (%)
1 109
7.3%
2 49
3.3%
3 30
 
2.0%
4 35
 
2.3%
5 55
3.7%
6 41
 
2.7%
7 44
2.9%
8 61
4.1%
9 40
 
2.7%
10 36
 
2.4%
ValueCountFrequency (%)
31 30
2.0%
30 40
2.7%
29 46
3.1%
28 43
2.9%
27 34
2.3%
26 40
2.7%
25 51
3.4%
24 52
3.5%
23 51
3.4%
22 41
2.7%

dayNReady
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Tuesday
287 
Wednesday
254 
Monday
247 
Thursday
235 
Sunday
230 
Other values (2)
246 

Length

Max length9
Median length8
Mean length7.2508339
Min length6

Characters and Unicode

Total characters10869
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSunday
2nd rowSunday
3rd rowSaturday
4th rowMonday
5th rowMonday

Common Values

ValueCountFrequency (%)
Tuesday 287
19.1%
Wednesday 254
16.9%
Monday 247
16.5%
Thursday 235
15.7%
Sunday 230
15.3%
Saturday 178
11.9%
Friday 68
 
4.5%

Length

2024-10-24T14:58:39.684570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-24T14:58:39.949219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
tuesday 287
19.1%
wednesday 254
16.9%
monday 247
16.5%
thursday 235
15.7%
sunday 230
15.3%
saturday 178
11.9%
friday 68
 
4.5%

Most occurring characters

ValueCountFrequency (%)
d 1753
16.1%
a 1677
15.4%
y 1499
13.8%
u 930
8.6%
e 795
7.3%
s 776
7.1%
n 731
6.7%
T 522
 
4.8%
r 481
 
4.4%
S 408
 
3.8%
Other values (7) 1297
11.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9370
86.2%
Uppercase Letter 1499
 
13.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 1753
18.7%
a 1677
17.9%
y 1499
16.0%
u 930
9.9%
e 795
8.5%
s 776
8.3%
n 731
7.8%
r 481
 
5.1%
o 247
 
2.6%
h 235
 
2.5%
Other values (2) 246
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
T 522
34.8%
S 408
27.2%
W 254
16.9%
M 247
16.5%
F 68
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 10869
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 1753
16.1%
a 1677
15.4%
y 1499
13.8%
u 930
8.6%
e 795
7.3%
s 776
7.1%
n 731
6.7%
T 522
 
4.8%
r 481
 
4.4%
S 408
 
3.8%
Other values (7) 1297
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10869
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 1753
16.1%
a 1677
15.4%
y 1499
13.8%
u 930
8.6%
e 795
7.3%
s 776
7.1%
n 731
6.7%
T 522
 
4.8%
r 481
 
4.4%
S 408
 
3.8%
Other values (7) 1297
11.9%

service_duration
Real number (ℝ)

Distinct21
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8819213
Minimum1
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2024-10-24T14:58:40.211914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile7
Maximum70
Range69
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.2858986
Coefficient of variation (CV)1.746034
Kurtosis186.02043
Mean1.8819213
Median Absolute Deviation (MAD)0
Skewness11.041562
Sum2821
Variance10.79713
MonotonicityNot monotonic
2024-10-24T14:58:40.425781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 1244
83.0%
5 42
 
2.8%
4 42
 
2.8%
2 39
 
2.6%
6 30
 
2.0%
7 26
 
1.7%
3 25
 
1.7%
8 17
 
1.1%
11 11
 
0.7%
9 7
 
0.5%
Other values (11) 16
 
1.1%
ValueCountFrequency (%)
1 1244
83.0%
2 39
 
2.6%
3 25
 
1.7%
4 42
 
2.8%
5 42
 
2.8%
6 30
 
2.0%
7 26
 
1.7%
8 17
 
1.1%
9 7
 
0.5%
10 2
 
0.1%
ValueCountFrequency (%)
70 1
 
0.1%
57 1
 
0.1%
32 1
 
0.1%
30 1
 
0.1%
24 1
 
0.1%
18 1
 
0.1%
17 2
0.1%
14 3
0.2%
13 1
 
0.1%
12 2
0.1%

cost_category
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
0001:0050
731 
0050:0100
308 
0100:0150
164 
0200:0300
157 
0300:0400
 
37
Other values (10)
102 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters13491
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st row0150:0200
2nd row0200:0300
3rd row0050:0100
4th row0200:0300
5th row0200:0300

Common Values

ValueCountFrequency (%)
0001:0050 731
48.8%
0050:0100 308
20.5%
0100:0150 164
 
10.9%
0200:0300 157
 
10.5%
0300:0400 37
 
2.5%
0150:0200 30
 
2.0%
0400:0500 26
 
1.7%
0500:0600 20
 
1.3%
0600:0700 10
 
0.7%
1000:1500 6
 
0.4%
Other values (5) 10
 
0.7%

Length

2024-10-24T14:58:40.669922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0001:0050 731
48.8%
0050:0100 308
20.5%
0100:0150 164
 
10.9%
0200:0300 157
 
10.5%
0300:0400 37
 
2.5%
0150:0200 30
 
2.0%
0400:0500 26
 
1.7%
0500:0600 20
 
1.3%
0600:0700 10
 
0.7%
1000:1500 6
 
0.4%
Other values (5) 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 8793
65.2%
: 1499
 
11.1%
1 1411
 
10.5%
5 1286
 
9.5%
3 196
 
1.5%
2 190
 
1.4%
4 63
 
0.5%
6 30
 
0.2%
7 15
 
0.1%
8 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11992
88.9%
Other Punctuation 1499
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8793
73.3%
1 1411
 
11.8%
5 1286
 
10.7%
3 196
 
1.6%
2 190
 
1.6%
4 63
 
0.5%
6 30
 
0.3%
7 15
 
0.1%
8 6
 
0.1%
9 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
: 1499
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13491
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8793
65.2%
: 1499
 
11.1%
1 1411
 
10.5%
5 1286
 
9.5%
3 196
 
1.5%
2 190
 
1.4%
4 63
 
0.5%
6 30
 
0.2%
7 15
 
0.1%
8 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13491
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8793
65.2%
: 1499
 
11.1%
1 1411
 
10.5%
5 1286
 
9.5%
3 196
 
1.5%
2 190
 
1.4%
4 63
 
0.5%
6 30
 
0.2%
7 15
 
0.1%
8 6
 
< 0.1%

Interactions

2024-10-24T14:58:16.925781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:17.762695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:24.091797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:35.219727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:39.294922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:43.823242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:47.405273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:51.467773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:54.939453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:58.939453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:02.365234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:06.208984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:09.595703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:12.943359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:17.293945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:18.732422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:24.546875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:35.478516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:39.540039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:44.084961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:47.661133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:51.732422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:55.206055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:59.188476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:02.606445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:06.456054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:09.844727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:13.193359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:17.543945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:19.090820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:24.899414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:35.733398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:39.889648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:44.350586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:47.907227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:51.990234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:55.473633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:59.428711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:02.848633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:06.700195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:10.083008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:13.438477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:17.803711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:19.357422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:25.170898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:35.997070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:40.181641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:44.612305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:48.161133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:52.241211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:55.739258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:59.678711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:03.099609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:06.949219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:10.333008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:14.089844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:18.045898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:19.611328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:25.408203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:36.296875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:40.707031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:44.861328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:48.407227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:52.471679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:55.985351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:59.909180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:03.335938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:07.188476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:10.559570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:14.378906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:18.308594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:19.955078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:32.966797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:36.605469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:41.205078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:45.124023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:48.719727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:52.724609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:56.253906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:00.159179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:03.582031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:07.441406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:10.808594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:14.633789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:18.597656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:20.351562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:33.220703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:36.871094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:41.484375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:45.381836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:48.971679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:52.965820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:56.511719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:00.405273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:03.827148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:07.688476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:11.053711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:14.885742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:18.842773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:20.706055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:33.459961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:37.117188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:42.152344image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:45.636719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:49.326172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:53.238281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:56.762695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:00.636719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:04.111328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:07.921875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:11.290039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:15.119141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:19.109375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:21.053711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:33.724609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:37.393555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:42.405273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:45.911133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:49.587891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:53.513672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:57.070312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:00.895508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:04.364258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:08.178711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:11.541016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:15.382812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:19.347656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:21.476562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:33.959961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:37.641602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:42.636719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:46.149414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:50.213867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:53.744141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:57.316406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:01.125000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:04.590820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:08.408203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:11.767578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:15.618164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:19.584961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:21.801758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:34.229492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:37.883789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:42.868164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:46.392578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:50.452148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:53.971679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:57.566406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:01.410156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:04.882812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:08.642578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:11.992188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:15.909180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:19.848633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:23.126953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:34.474609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:38.233398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:43.105469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:46.642578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:50.703125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:54.208984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:58.178711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:01.647461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:05.142578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:08.875977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:12.229492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:16.145508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:20.085938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:23.496094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:34.715820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:38.640625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:43.340820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:46.887695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:50.949219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:54.442383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:58.424805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:01.876953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:05.370117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:09.107422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:12.459961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:16.379883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:20.335938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:23.797851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:34.959961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:38.986328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:43.575195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:47.143554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:51.201172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:54.693359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:57:58.676758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:02.121094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:05.603516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:09.348633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:12.697266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-10-24T14:58:16.614258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2024-10-24T14:58:40.975586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
#Fuel DiffFuel inFuel outKMs DiffKMs INKMs outUnnamed: 0carcostcost_categorydamage typedayIndayNIndayNReadydayReadydelivered bylocationmonthInmonthNInmonthNReadymonthReadyreturned byservice_durationyearInyearReady
#1.000-0.0430.1730.166-0.0840.1430.1431.0000.0730.1310.1440.1080.0590.1520.1280.0620.4490.2720.5410.7030.7020.5420.446-0.0370.8940.894
Fuel Diff-0.0431.000-0.0960.1630.127-0.006-0.006-0.0430.0000.0640.1380.062-0.0100.0250.0000.0010.1690.149-0.1980.1650.156-0.1960.1700.1130.2440.244
Fuel in0.173-0.0961.0000.948-0.010-0.010-0.0100.1730.0000.0510.0660.0550.0470.0650.0490.0440.2570.1970.1960.2110.2100.1950.261-0.0580.3890.389
Fuel out0.1660.1630.9481.0000.019-0.006-0.0060.1660.0000.0780.0880.0300.0480.0620.0400.0520.2480.1730.1350.2050.2040.1340.255-0.0150.3800.380
KMs Diff-0.0840.127-0.0100.0191.000-0.018-0.018-0.0840.0430.0600.0630.084-0.0050.0470.0470.0070.2080.120-0.0440.1890.190-0.0430.1980.0270.1190.119
KMs IN0.143-0.006-0.010-0.006-0.0181.0001.0000.1430.2280.0350.0490.0330.0010.0470.0370.0040.0000.0600.1080.0870.0870.1090.0000.0750.0440.044
KMs out0.143-0.006-0.010-0.006-0.0181.0001.0000.1430.2280.0350.0490.0330.0010.0470.0370.0040.0000.0600.1080.0870.0870.1090.0000.0750.0440.044
Unnamed: 01.000-0.0430.1730.166-0.0840.1430.1431.0000.0730.1310.1440.1080.0590.1520.1280.0620.4490.2720.5410.7030.7020.5420.446-0.0370.8940.894
car0.0730.0000.0000.0000.0430.2280.2280.0731.0000.0000.0580.0840.0760.0870.0720.0750.0310.0460.0670.0780.0770.0660.0340.0000.0000.000
cost0.1310.0640.0510.0780.0600.0350.0350.1310.0001.0000.8310.2480.0160.0000.0280.0510.0410.3120.1780.0900.0970.1850.0570.4600.1440.144
cost_category0.1440.1380.0660.0880.0630.0490.0490.1440.0580.8311.0000.3760.0560.0660.0470.0780.1090.2780.1350.1390.1430.1400.1160.4100.1710.171
damage type0.1080.0620.0550.0300.0840.0330.0330.1080.0840.2480.3761.0000.0840.0690.0610.0910.2030.6600.0880.1180.1170.0870.2170.1460.1040.104
dayIn0.059-0.0100.0470.048-0.0050.0010.0010.0590.0760.0160.0560.0841.0000.1560.1430.9150.1530.1220.0040.1910.1860.0110.158-0.0140.0730.073
dayNIn0.1520.0250.0650.0620.0470.0470.0470.1520.0870.0000.0660.0690.1561.0000.8070.1470.1280.1180.1470.1600.1620.1480.1200.0530.0740.074
dayNReady0.1280.0000.0490.0400.0470.0370.0370.1280.0720.0280.0470.0610.1430.8071.0000.1380.1310.1120.1410.1440.1480.1450.1200.0000.0950.095
dayReady0.0620.0010.0440.0520.0070.0040.0040.0620.0750.0510.0780.0910.9150.1470.1381.0000.1450.1160.0010.1600.160-0.0040.1540.0630.1070.107
delivered by0.4490.1690.2570.2480.2080.0000.0000.4490.0310.0410.1090.2030.1530.1280.1310.1451.0000.2240.4170.4030.4050.4210.9740.0980.5340.534
location0.2720.1490.1970.1730.1200.0600.0600.2720.0460.3120.2780.6600.1220.1180.1120.1160.2241.0000.2210.2350.2330.2200.2330.0930.5960.596
monthIn0.541-0.1980.1960.135-0.0440.1080.1080.5410.0670.1780.1350.0880.0040.1470.1410.0010.4170.2211.0000.9990.9780.9990.415-0.1030.6300.630
monthNIn0.7030.1650.2110.2050.1890.0870.0870.7030.0780.0900.1390.1180.1910.1600.1440.1600.4030.2350.9991.0000.9800.9780.3920.0650.7090.709
monthNReady0.7020.1560.2100.2040.1900.0870.0870.7020.0770.0970.1430.1170.1860.1620.1480.1600.4050.2330.9780.9801.0000.9990.3940.0620.7020.702
monthReady0.542-0.1960.1950.134-0.0430.1090.1090.5420.0660.1850.1400.0870.0110.1480.145-0.0040.4210.2200.9990.9780.9991.0000.419-0.0900.6350.635
returned by0.4460.1700.2610.2550.1980.0000.0000.4460.0340.0570.1160.2170.1580.1200.1200.1540.9740.2330.4150.3920.3940.4191.0000.1020.5340.534
service_duration-0.0370.113-0.058-0.0150.0270.0750.075-0.0370.0000.4600.4100.146-0.0140.0530.0000.0630.0980.093-0.1030.0650.062-0.0900.1021.0000.1310.131
yearIn0.8940.2440.3890.3800.1190.0440.0440.8940.0000.1440.1710.1040.0730.0740.0950.1070.5340.5960.6300.7090.7020.6350.5340.1311.0000.996
yearReady0.8940.2440.3890.3800.1190.0440.0440.8940.0000.1440.1710.1040.0730.0740.0950.1070.5340.5960.6300.7090.7020.6350.5340.1310.9961.000

Missing values

2024-10-24T14:58:20.975586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-24T14:58:22.328125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0#plate numbercardamage typedate inKMs INFuel indate readyKMs outKMs DiffFuel outFuel Diffcostlocationcorporatedelivered byreturned bynotesyearInmonthInmonthNIndayIndayNInyearReadymonthReadymonthNReadydayReadydayNReadyservice_durationcost_category
00170-29280TUCSANاصلاح بودي2015-01-14230150.132015-01-1823030150.130.00150المركزيةXeOmar MOmar Mاصلاح بودي ضربة باب خلفي شمال المركزية20151January14Wednesday20151January18Sunday50150:0200
11270-26587ELANTRAاصلاح بودي2015-01-14436380.382015-01-184363800.380.00281المركزيةXeMaenMaenاصلاح بودي ضربة مرش يمين المركزية20151January14Wednesday20151January18Sunday50200:0300
22370-25180AVANZAاصلاح مكانيك2015-01-14398800.382015-01-243988990.750.3792المركزيةXeMohamad JMohamad Jاصلاح حميان المركزية20151January14Wednesday20151January24Saturday110050:0100
33470-26523FLUENCEاصلاح بودي2015-01-14437050.132015-01-1943725200.250.12250ابو خضرXeMohamad QasimMohamad Qasimغيار مراة كاملة شركة رنوت20151January14Wednesday20151January19Monday60200:0300
44570-30719FLUENCEغيار زيت2015-01-14251450.132015-01-1925160150.130.00253المركزيةXeMohamad QasimOmar Mغيار زيت + اصلاح مرشة الغزاوي/ المركزية20151January14Wednesday20151January19Monday60200:0300
55670-25207COROLLAاصلاح بودي2015-01-14853730.632015-01-2185383100.630.00650المركزيةشركه الهندسه الكهروميكانكيهMohamad QasimMohamad Qasimاصلاح ضربة امامية المركزية20151January14Wednesday20151January21Wednesday80600:0700
66770-31356CAMRYاصلاح كوشوك2015-01-14317081.002015-01-1431719111.000.00320هانكونكVestasMohamad QasimMohamad Qasimغيار 4 كاوشوك هانكونك فيستاس20151January14Wednesday20151January14Wednesday10300:0400
77870-24459FORTUNERغيار زيت2015-01-16694750.382015-01-166948380.500.1221الغزاويXeMohamad JMohamad Jغيار زيت20151January16Friday20151January16Friday10001:0050
88916-94807PRADOغيار زيت2015-01-17251050.132015-01-1725121160.00-0.1321الغزاويXeMohamad QasimMaenغيار زيت20151January17Saturday20151January17Saturday10001:0050
991070-24426FORTUNERغيار زيت2015-01-17575010.002015-01-1757512110.000.0021الغزاويXeMohamad QasimMohamad Qasimغيار زيت20151January17Saturday20151January17Saturday10001:0050
Unnamed: 0#plate numbercardamage typedate inKMs INFuel indate readyKMs outKMs DiffFuel outFuel Diffcostlocationcorporatedelivered byreturned bynotesyearInmonthInmonthNIndayIndayNInyearReadymonthReadymonthNReadydayReadydayNReadyservice_durationcost_category
14891489149070-25363ELANTRAغيار زيت2016-02-011038050.502016-02-0110380940.500.0021الغزاوي+معاذXeAladdin RAladdin Rغيار زيت+ تشيك كامل20162February1Monday20162February1Monday10001:0050
14901490149170-26739CERATOاصلاح كوشوك2016-02-01919370.132016-02-0191962250.250.1215المركزيه+زكيXeAbdallaAbdallaجنط مضروب20162February1Monday20162February1Monday10001:0050
14911491149270-24540SPORTAGEغيار زيت2016-02-01805550.382016-02-018055940.380.0021كياXeAmjadAmjadغيار زيت+ تشيك كامل20162February1Monday20162February1Monday10001:0050
14921492149370-24127RIOاصلاح بودي2016-02-021150650.252016-02-0211507490.250.0020معاذ عليانXeTareqTareqغطاء بنزين20162February2Tuesday20162February2Tuesday10001:0050
14931493149439-50567HILUXاصلاح مكانيك2016-02-0295300.752016-02-029545150.750.0020المركزيةThe Risk Advisory GroupDirarDirarتشيك كامل +تقطيعه20162February2Tuesday20162February2Tuesday10001:0050
14941494149570-25213COROLLAاصلاح كوشوك2016-02-02841660.002016-02-028416930.000.00245ابو خضرXeMohamad QasimMohamad Qasimغيار زيت+ تشيك كامل+اطارات20162February2Tuesday20162February2Tuesday10200:0300
14951495149670-29981TUCSANغيار زيت2016-02-02601220.252016-02-026012640.250.0021هانكونكXeAmjadAmjadغيار زيت+ تشيك كامل20162February2Tuesday20162February2Tuesday10001:0050
14961496149770-29538COROLLAاصلاح بودي2016-01-27681490.132016-02-036815450.250.12200الغزاويXeTareqTareqضربة جناح خلفي يمين20161January27Wednesday20162February3Wednesday80200:0300
14971497149870-35613CAMRYاصلاح بودي2016-01-31437160.252016-02-034372480.380.1380اليادودةXeTareqTareqطبون خلفي20161January31Sunday20162February3Wednesday40050:0100
14981498149970-34029FORTUNERغيار زيت2016-02-03301280.382016-02-033013680.380.0021المكتبSave The ChildrenTareqTareqغيار زيت+ تشيك كامل20162February3Wednesday20162February3Wednesday10001:0050